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Merge branch 'release/2.0' into faq_2.0

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12
PPOCRLabel/README.md
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@ -9,7 +9,7 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, w
### Recent Update
- 2021.1.11: Optimize the labeling experience (by [edencfc](https://github.com/edencfc)),
- Users can choose whether to pop up the label input dialog after drawing the detection box in "View - Pop-up Label Input Dialog".
- Users can choose whether to pop up the label input dialog after drawing the detection box in "View - Pop-up Label Input Dialog".
- The recognition result scrolls synchronously when users click related detection box.
- Click to modify the recognition result.(If you can't change the result, please switch to the system default input method, or switch back to the original input method again)
- 2020.12.18: Support re-recognition of a single label box (by [ninetailskim](https://github.com/ninetailskim) ), perfect shortcut keys.
@ -49,7 +49,7 @@ python3 PPOCRLabel.py
```
pip3 install pyqt5
pip3 uninstall opencv-python # Uninstall opencv manually as it conflicts with pyqt
pip3 install opencv-contrib-python-headless # Install the headless version of opencv
pip3 install opencv-contrib-python-headless==4.2.0.32 # Install the headless version of opencv
cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
python3 PPOCRLabel.py
```
@ -127,7 +127,7 @@ Therefore, if the recognition result has been manually changed before, it may ch
- Default model: PPOCRLabel uses the Chinese and English ultra-lightweight OCR model in PaddleOCR by default, supports Chinese, English and number recognition, and multiple language detection.
- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languages​include French, German, Korean, and Japanese.
- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languages​include French, German, Korean, and Japanese.
For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md#multilingual-recognition-modelupdating)
- Custom model: The model trained by users can be replaced by modifying PPOCRLabel.py in [PaddleOCR class instantiation](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/PPOCRLabel.py#L110) referring [Custom Model Code](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md#use-custom-model)
@ -160,11 +160,11 @@ For some data that are difficult to recognize, the recognition results will not
```
pyrcc5 -o libs/resources.py resources.qrc
```
- If you get an error ``` module 'cv2' has no attribute 'INTER_NEAREST'```, you need to delete all opencv related packages first, and then reinstall the headless version of opencv
- If you get an error ``` module 'cv2' has no attribute 'INTER_NEAREST'```, you need to delete all opencv related packages first, and then reinstall the 4.2.0.32 version of headless opencv
```
pip install opencv-contrib-python-headless
pip install opencv-contrib-python-headless==4.2.0.32
```
### Related
1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)

12
PPOCRLabel/README_ch.md
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@ -49,7 +49,7 @@ python3 PPOCRLabel.py --lang ch
```
pip3 install pyqt5
pip3 uninstall opencv-python # 由于mac版本的opencv与pyqt有冲突，需先手动卸载opencv
pip3 install opencv-contrib-python-headless # 安装headless版本的open-cv
pip3 install opencv-contrib-python-headless==4.2.0.32 # 安装headless版本的open-cv
cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
python3 PPOCRLabel.py --lang ch
```
@ -132,22 +132,22 @@ PPOCRLabel支持三种保存方式：
### 错误提示
- 如果同时使用whl包安装了paddleocr，其优先级大于通过paddleocr.py调用PaddleOCR类，whl包未更新时会导致程序异常。
- PPOCRLabel**不支持对中文文件名**的图片进行自动标注。
- 针对Linux用户：如果您在打开软件过程中出现**objc[XXXXX]**开头的错误，证明您的opencv版本太高，建议安装4.2版本：
```
pip install opencv-python==4.2.0.32
```
- 如果出现 ```Missing string id``` 开头的错误，需要重新编译资源：
```
pyrcc5 -o libs/resources.py resources.qrc
```
- 如果出现``` module 'cv2' has no attribute 'INTER_NEAREST'```错误，需要首先删除所有opencv相关包，然后重新安装headless版本的opencv
- 如果出现``` module 'cv2' has no attribute 'INTER_NEAREST'```错误，需要首先删除所有opencv相关包，然后重新安装4.2.0.32版本的headless opencv
```
pip install opencv-contrib-python-headless
pip install opencv-contrib-python-headless==4.2.0.32
```
### 参考资料

1
README.md
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@ -9,6 +9,7 @@ PaddleOCR supports both dynamic graph and static graph programming paradigm
- Static graph: develop branch
**Recent updates**
- 2021.2.8 Release PaddleOCRv2.0(branch release/2.0) and set as default branch. Check release note here: https://github.com/PaddlePaddle/PaddleOCR/releases/tag/v2.0.0
- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.

1
README_ch.md
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@ -8,6 +8,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- 静态图版本：develop分支
**近期更新**
- 2021.2.8 正式发布PaddleOCRv2.0(branch release/2.0)并设置为推荐用户使用的默认分支. 发布的详细内容，请参考: https://github.com/PaddlePaddle/PaddleOCR/releases/tag/v2.0.0
- 2021.2.8 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数167个，每周一都会更新，欢迎大家持续关注。
- 2021.1.26,28,29 PaddleOCR官方研发团队带来技术深入解读三日直播课，1月26日、28日、29日晚上19:30，[直播地址](https://live.bilibili.com/21689802)
- 2021.1.21 更新多语言识别模型，目前支持语种超过27种，[多语言模型下载](./doc/doc_ch/models_list.md)，包括中文简体、中文繁体、英文、法文、德文、韩文、日文、意大利文、西班牙文、葡萄牙文、俄罗斯文、阿拉伯文等，后续计划可以参考[多语言研发计划](https://github.com/PaddlePaddle/PaddleOCR/issues/1048)

4
configs/rec/rec_mv3_tps_bilstm_att.yml
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@ -66,7 +66,7 @@ Metric:
Train:
dataset:
name: LMDBDataSet
data_dir: ../training/
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
img_mode: BGR
@ -85,7 +85,7 @@ Train:
Eval:
dataset:
name: LMDBDataSet
data_dir: ../validation/
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
img_mode: BGR

4
configs/rec/rec_r34_vd_tps_bilstm_att.yml
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@ -65,7 +65,7 @@ Metric:
Train:
dataset:
name: LMDBDataSet
data_dir: ../training/
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
img_mode: BGR
@ -84,7 +84,7 @@ Train:
Eval:
dataset:
name: LMDBDataSet
data_dir: ../validation/
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
img_mode: BGR

4
configs/rec/rec_r50_fpn_srn.yml
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@ -59,7 +59,7 @@ Metric:
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/srn_train_data_duiqi
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
img_mode: BGR
@ -84,7 +84,7 @@ Train:
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
img_mode: BGR

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4
ppocr/data/imaug/label_ops.py
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@ -215,7 +215,7 @@ class AttnLabelEncode(BaseRecLabelEncode):
return None
data['length'] = np.array(len(text))
text = [0] + text + [len(self.character) - 1] + [0] * (self.max_text_len
- len(text) - 1)
- len(text) - 2)
data['label'] = np.array(text)
return data
@ -261,7 +261,7 @@ class SRNLabelEncode(BaseRecLabelEncode):
if len(text) > self.max_text_len:
return None
data['length'] = np.array(len(text))
text = text + [char_num] * (self.max_text_len - len(text))
text = text + [char_num - 1] * (self.max_text_len - len(text))
data['label'] = np.array(text)
return data

3
ppocr/modeling/heads/rec_att_head.py
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@ -57,6 +57,9 @@ class AttentionHead(nn.Layer):
else:
targets = paddle.zeros(shape=[batch_size], dtype="int32")
probs = None
char_onehots = None
outputs = None
alpha = None
for i in range(num_steps):
char_onehots = self._char_to_onehot(

3
requirements.txt
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@ -1,5 +1,6 @@
shapely
imgaug
scikit-image==0.17.2
imgaug==0.4.0
pyclipper
lmdb
opencv-python==4.2.0.32

3
tools/eval.py
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@ -47,6 +47,7 @@ def main():
config['Architecture']["Head"]['out_channels'] = len(
getattr(post_process_class, 'character'))
model = build_model(config['Architecture'])
use_srn = config['Architecture']['algorithm'] == "SRN"
best_model_dict = init_model(config, model, logger)
if len(best_model_dict):
@ -59,7 +60,7 @@ def main():
# start eval
metirc = program.eval(model, valid_dataloader, post_process_class,
eval_class)
eval_class, use_srn)
logger.info('metric eval ***************')
for k, v in metirc.items():
logger.info('{}:{}'.format(k, v))

7
tools/infer/predict_rec.py
View File

@ -54,6 +54,13 @@ class TextRecognizer(object):
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "RARE":
postprocess_params = {
'name': 'AttnLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = \
utility.create_predictor(args, 'rec', logger)

18
tools/program.py
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@ -182,6 +182,8 @@ def train(config,
model_average = False
model.train()
use_srn = config['Architecture']['algorithm'] == "SRN"
if 'start_epoch' in best_model_dict:
start_epoch = best_model_dict['start_epoch']
else:
@ -200,7 +202,7 @@ def train(config,
break
lr = optimizer.get_lr()
images = batch[0]
if config['Architecture']['algorithm'] == "SRN":
if use_srn:
others = batch[-4:]
preds = model(images, others)
model_average = True
@ -256,8 +258,12 @@ def train(config,
min_average_window=10000,
max_average_window=15625)
Model_Average.apply()
cur_metric = eval(model, valid_dataloader, post_process_class,
eval_class)
cur_metric = eval(
model,
valid_dataloader,
post_process_class,
eval_class,
use_srn=use_srn)
cur_metric_str = 'cur metric, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
logger.info(cur_metric_str)
@ -321,7 +327,8 @@ def train(config,
return
def eval(model, valid_dataloader, post_process_class, eval_class):
def eval(model, valid_dataloader, post_process_class, eval_class,
use_srn=False):
model.eval()
with paddle.no_grad():
total_frame = 0.0
@ -332,7 +339,8 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
break
images = batch[0]
start = time.time()
if "SRN" in str(model.head):
if use_srn:
others = batch[-4:]
preds = model(images, others)
else: